Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding

Novel risk score model for non-proliferative diabetic retinopathy based on untargeted metabolomics of venous blood

Background and Purpose

Nonproliferative diabetic retinopathy (NPDR) occurs in the early stages of Diabetic retinopathy (DR), and the study of its metabolic markers will help to prevent DR. Hence, we aimed to establish a risk score based on multiple metabolites through untargeted metabolomic analysis of venous blood from NPDR patients and diabetic non-DR patients.

Experimental Approach

Untargeted metabolomics of venous blood samples from patients with NPDR, diabetes melitus without DR were performed using high-performance liquid chromatography-mass spectrometry.

Results

Detailed metabolomic evaluation showed distinct clusters of metabolites in plasma samples from patients with NPDR and diabetic non-DR patients. NPDR patients had significantly higher levels of phenylacetylglycine, L-aspartic acid, tiglylglycine, and 3-sulfinato-L-alaninate, and lower level of indolelactic acid, threonic acid, L-arginine (Arg), and 4-dodecylbenzenesulfonic acid compared to control. The expression profiles of these eight NPDR risk-related characteristic metabolites were analyzed using Cox regression to establish a risk score model. Subsequently, univariate and multivariate Cox regression analyses were used to determine that this risk score model was a predictor of independent prognosis for NPDR.

Conclusions

Untargeted metabolome analysis of blood metabolites revealed unreported metabolic alterations in NPDR patients compared with those in diabetic non-DR patients or MH. In the venous blood, we identified depleted metabolites thA and Arg, indicating that they might play a role in NPDR development.

Differential expression of MDGA1 in major depressive disorder

The identification of gene expression-based biomarkers for major depressive disorder (MDD) continues to be an important challenge. In order to identify candidate biomarkers and mechanisms, we apply statistical and machine learning feature selection to an RNA-Seq gene expression dataset of 78 unmedicated individuals with MDD and 79 healthy controls. We identify 49 genes by LASSO penalized logistic regression and 45 genes at the false discovery rate threshold 0.188. The MDGA1 gene has the lowest P-value (4.9e-5) and is expressed in the developing brain, involved in axon guidance, and associated with related mood disorders in previous studies of bipolar disorder (BD) and schizophrenia (SCZ). The expression of MDGA1 is associated with age and sex, but its association with MDD remains significant when adjusted for covariates. MDGA1 is in a co-expression cluster with another top gene, ATXN7L2 (ataxin 7 like 2), which was associated with MDD in a recent GWAS. The LASSO classification model of MDD includes MDGA1, and the model has a cross-validation accuracy of 79%. Another noteworthy top gene, IRF2BPL, is in a close co-expression cluster with MDGA1 and may be related to microglial inflammatory states in MDD. Future exploration of MDGA1 and its gene interactions may provide insights into mechanisms and heterogeneity of MDD.

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We use penalized regression to select differentially expressed genes and characterize their relationships through clustering.

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We identify MDGA1 as the most differentially expressed gene between MDD and healthy controls using RNA-Seq.

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Previous studies have implicated MDGA1 in psychiatric disorders, such as schizophrenia and bipolar disorder, but not in MDD.

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Different psychiatric disorders have some genetic associations in common due to shared neural pathways between disorders.

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A top gene, IRF2BPL, in a close co-expression cluster with MDGA1 may be related to microglial inflammatory states in MDD.

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Future investigation of interactions of MDGA1 and IRF2BPL may provide insights into mechanisms and heterogeneity of MDD.

Novel HLA associations with outcomes of Mycobacterium tuberculosis exposure and sarcoidosis in individuals of African ancestry using nearest-neighbor feature selection

Tuberculosis and sarcoidosis are inflammatory diseases characterized by granulomas that may occur in any organ but are often found in the lung. The panoply of classical human leukocyte antigen (HLA) alleles associated with occurrence and/or severity of both diseases varies considerably across studies. This heterogeneity of results, due to variation in factors like ancestry and disease subphenotype, as well as the use of simple modeling strategies to elucidate likely complex relationships, has made conclusions about underlying commonalities difficult. Here we perform HLA association analyses in individuals of African ancestry, using a greater resolution to include subphenotypes of disease and employing more comprehensive analytical techniques. Using a novel application of nearest-neighbor feature selection to score allelic importance, we investigated HLA allele association with Mycobacterium tuberculosis exposure outcomes in the first analysis of both latent Mycobacterium tuberculosis infection and active disease compared with those who, despite long-term exposure to active index cases, have neither positive diagnostic tests nor display clinical symptoms. We also compared persistent to resolved sarcoidosis. This led to the identification of novel HLA associations and evidence of main effects and interaction effects. We found strikingly similar main effects and interaction effects at HLA-DRB1, -DQB1, and -DPB1 in those resistant to tuberculosis (either latent or active) and persistent sarcoidosis.

Theoretical properties of distance distributions and novel metrics for nearest-neighbor feature selection

The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. Recent work to improve nearest-neighbor feature selection algorithms has focused on new neighborhood estimation methods and distance metrics. However, little attention has been given to the distributional properties of pairwise distances as a function of the metric or data type. Thus, we derive general analytical expressions for the mean and variance of pairwise distances for L_q metrics for normal and uniform random data with p attributes and m instances. The distribution moment formulas and detailed derivations provide a resource for understanding the distance properties for metrics and data types commonly used with nearest-neighbor methods, and the derivations provide the starting point for the following novel results. We use extreme value theory to derive the mean and variance for metrics that are normalized by the range of each attribute (difference of max and min). We derive analytical formulas for a new metric for genetic variants, which are categorical variables that occur in genome-wide association studies (GWAS). The genetic distance distributions account for minor allele frequency and the transition/transversion ratio. We introduce a new metric for resting-state functional MRI data (rs-fMRI) and derive its distance distribution properties. This metric is applicable to correlation-based predictors derived from time-series data. The analytical means and variances are in strong agreement with simulation results. We also use simulations to explore the sensitivity of the expected means and variances in the presence of correlation and interactions in the data. These analytical results and new metrics can be used to inform the optimization of nearest neighbor methods for a broad range of studies, including gene expression, GWAS, and fMRI data.

Nearest-Neighbor Projected Distance Regression for Epistasis Detection in GWAS With Population Structure Correction

Nearest-neighbor Projected-Distance Regression (NPDR) is a feature selection technique that uses nearest-neighbors in high dimensional data to detect complex multivariate effects including epistasis. NPDR uses a regression formalism that allows statistical significance testing and efficient control for multiple testing. In addition, the regression formalism provides a mechanism for NPDR to adjust for population structure, which we apply to a GWAS of systemic lupus erythematosus (SLE). We also test NPDR on benchmark simulated genetic variant data with epistatic effects, main effects, imbalanced data for case-control design and continuous outcomes. NPDR identifies potential interactions in an epistasis network that influences the SLE disorder.